--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:3002496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. datasets: - sentence-transformers/gooaq pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 co2_eq_emissions: emissions: 7.447488216858034 energy_consumed: 0.019159891682723612 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.124 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BEE-spoke-data/wordpiece-tokenizer-32k-en_code-msp tokenizer finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 1024 dev type: gooaq-1024-dev metrics: - type: cosine_accuracy@1 value: 0.6335 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8394 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8979 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9454 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6335 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27979999999999994 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17958000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09454000000000003 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6335 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8394 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8979 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9454 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7948890776997601 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7459194047618989 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7484214498572738 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 512 dev type: gooaq-512-dev metrics: - type: cosine_accuracy@1 value: 0.6285 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8339 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8943 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9425 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6285 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2779666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17886000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09425000000000003 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6285 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8339 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8943 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9425 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7907464684784297 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7413761111111041 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7439975831469758 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 256 dev type: gooaq-256-dev metrics: - type: cosine_accuracy@1 value: 0.6196 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8262 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.888 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9375 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6196 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2754 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17760000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09375000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6196 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8262 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.888 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9375 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7830118342115728 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.73284916666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7356198073355731 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 128 dev type: gooaq-128-dev metrics: - type: cosine_accuracy@1 value: 0.597 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8033 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8681 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9247 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.597 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26776666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17361999999999997 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09247000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.597 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8033 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8681 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9247 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7633008182074578 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7111824206349133 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.714297170282837 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 64 dev type: gooaq-64-dev metrics: - type: cosine_accuracy@1 value: 0.5541 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7568 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8287 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.896 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5541 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25226666666666664 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16574 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08960000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5541 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7568 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8287 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.896 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7246476170472534 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6696768650793602 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6735610073887002 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 32 dev type: gooaq-32-dev metrics: - type: cosine_accuracy@1 value: 0.4602 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6631 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7372 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8226 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4602 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22103333333333336 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14744000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08225999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4602 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6631 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7372 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8226 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6372411594771165 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5783468650793636 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5841294309265819 name: Cosine Map@100 --- # Static Embeddings with BEE-spoke-data/wordpiece-tokenizer-32k-en_code-msp tokenizer finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. See [train_script.py](train_script.py) for the training script used to train this model. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(31999, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/static-BEE-spoke-data-tokenizer-v2-gooaq") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `gooaq-1024-dev` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 1024 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6335 | | cosine_accuracy@3 | 0.8394 | | cosine_accuracy@5 | 0.8979 | | cosine_accuracy@10 | 0.9454 | | cosine_precision@1 | 0.6335 | | cosine_precision@3 | 0.2798 | | cosine_precision@5 | 0.1796 | | cosine_precision@10 | 0.0945 | | cosine_recall@1 | 0.6335 | | cosine_recall@3 | 0.8394 | | cosine_recall@5 | 0.8979 | | cosine_recall@10 | 0.9454 | | **cosine_ndcg@10** | **0.7949** | | cosine_mrr@10 | 0.7459 | | cosine_map@100 | 0.7484 | #### Information Retrieval * Dataset: `gooaq-512-dev` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 512 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6285 | | cosine_accuracy@3 | 0.8339 | | cosine_accuracy@5 | 0.8943 | | cosine_accuracy@10 | 0.9425 | | cosine_precision@1 | 0.6285 | | cosine_precision@3 | 0.278 | | cosine_precision@5 | 0.1789 | | cosine_precision@10 | 0.0943 | | cosine_recall@1 | 0.6285 | | cosine_recall@3 | 0.8339 | | cosine_recall@5 | 0.8943 | | cosine_recall@10 | 0.9425 | | **cosine_ndcg@10** | **0.7907** | | cosine_mrr@10 | 0.7414 | | cosine_map@100 | 0.744 | #### Information Retrieval * Dataset: `gooaq-256-dev` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 256 } ``` | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.6196 | | cosine_accuracy@3 | 0.8262 | | cosine_accuracy@5 | 0.888 | | cosine_accuracy@10 | 0.9375 | | cosine_precision@1 | 0.6196 | | cosine_precision@3 | 0.2754 | | cosine_precision@5 | 0.1776 | | cosine_precision@10 | 0.0938 | | cosine_recall@1 | 0.6196 | | cosine_recall@3 | 0.8262 | | cosine_recall@5 | 0.888 | | cosine_recall@10 | 0.9375 | | **cosine_ndcg@10** | **0.783** | | cosine_mrr@10 | 0.7328 | | cosine_map@100 | 0.7356 | #### Information Retrieval * Dataset: `gooaq-128-dev` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 128 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.597 | | cosine_accuracy@3 | 0.8033 | | cosine_accuracy@5 | 0.8681 | | cosine_accuracy@10 | 0.9247 | | cosine_precision@1 | 0.597 | | cosine_precision@3 | 0.2678 | | cosine_precision@5 | 0.1736 | | cosine_precision@10 | 0.0925 | | cosine_recall@1 | 0.597 | | cosine_recall@3 | 0.8033 | | cosine_recall@5 | 0.8681 | | cosine_recall@10 | 0.9247 | | **cosine_ndcg@10** | **0.7633** | | cosine_mrr@10 | 0.7112 | | cosine_map@100 | 0.7143 | #### Information Retrieval * Dataset: `gooaq-64-dev` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 64 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5541 | | cosine_accuracy@3 | 0.7568 | | cosine_accuracy@5 | 0.8287 | | cosine_accuracy@10 | 0.896 | | cosine_precision@1 | 0.5541 | | cosine_precision@3 | 0.2523 | | cosine_precision@5 | 0.1657 | | cosine_precision@10 | 0.0896 | | cosine_recall@1 | 0.5541 | | cosine_recall@3 | 0.7568 | | cosine_recall@5 | 0.8287 | | cosine_recall@10 | 0.896 | | **cosine_ndcg@10** | **0.7246** | | cosine_mrr@10 | 0.6697 | | cosine_map@100 | 0.6736 | #### Information Retrieval * Dataset: `gooaq-32-dev` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 32 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4602 | | cosine_accuracy@3 | 0.6631 | | cosine_accuracy@5 | 0.7372 | | cosine_accuracy@10 | 0.8226 | | cosine_precision@1 | 0.4602 | | cosine_precision@3 | 0.221 | | cosine_precision@5 | 0.1474 | | cosine_precision@10 | 0.0823 | | cosine_recall@1 | 0.4602 | | cosine_recall@3 | 0.6631 | | cosine_recall@5 | 0.7372 | | cosine_recall@10 | 0.8226 | | **cosine_ndcg@10** | **0.6372** | | cosine_mrr@10 | 0.5783 | | cosine_map@100 | 0.5841 | ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,002,496 training samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is the difference between broilers and layers? | An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well. | | what is the difference between chronological order and spatial order? | As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time. | | is kamagra same as viagra? | Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 10,000 evaluation samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how do i program my directv remote with my tv? | ['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.'] | | are rodrigues fruit bats nocturnal? | Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night. | | why does your heart rate increase during exercise bbc bitesize? | During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | gooaq-1024-dev_cosine_ndcg@10 | gooaq-512-dev_cosine_ndcg@10 | gooaq-256-dev_cosine_ndcg@10 | gooaq-128-dev_cosine_ndcg@10 | gooaq-64-dev_cosine_ndcg@10 | gooaq-32-dev_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------:| | -1 | -1 | - | - | 0.2340 | 0.2217 | 0.1954 | 0.1493 | 0.0863 | 0.0339 | | 0.0007 | 1 | 35.6378 | - | - | - | - | - | - | - | | 0.0682 | 100 | 16.3559 | - | - | - | - | - | - | - | | 0.1363 | 200 | 6.0576 | - | - | - | - | - | - | - | | 0.1704 | 250 | - | 1.6966 | 0.7315 | 0.7266 | 0.7170 | 0.6895 | 0.6443 | 0.5363 | | 0.2045 | 300 | 4.9232 | - | - | - | - | - | - | - | | 0.2727 | 400 | 4.4397 | - | - | - | - | - | - | - | | 0.3408 | 500 | 4.1373 | 1.4008 | 0.7613 | 0.7561 | 0.7459 | 0.7253 | 0.6838 | 0.5866 | | 0.4090 | 600 | 3.8967 | - | - | - | - | - | - | - | | 0.4772 | 700 | 3.732 | - | - | - | - | - | - | - | | 0.5112 | 750 | - | 1.2860 | 0.7749 | 0.7708 | 0.7630 | 0.7413 | 0.7017 | 0.6096 | | 0.5453 | 800 | 3.6054 | - | - | - | - | - | - | - | | 0.6135 | 900 | 3.4792 | - | - | - | - | - | - | - | | 0.6817 | 1000 | 3.4143 | 1.1877 | 0.7847 | 0.7806 | 0.7729 | 0.7524 | 0.7119 | 0.6212 | | 0.7498 | 1100 | 3.3194 | - | - | - | - | - | - | - | | 0.8180 | 1200 | 3.2469 | - | - | - | - | - | - | - | | 0.8521 | 1250 | - | 1.1253 | 0.7928 | 0.7888 | 0.7805 | 0.7612 | 0.7221 | 0.6337 | | 0.8862 | 1300 | 3.2015 | - | - | - | - | - | - | - | | 0.9543 | 1400 | 3.1689 | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.7949 | 0.7907 | 0.7830 | 0.7633 | 0.7246 | 0.6372 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.019 kWh - **Carbon Emitted**: 0.007 kg of CO2 - **Hours Used**: 0.124 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```